Novel Bayesian algorithms for ARFIMA long-memory processes: a comparison between MCMC and ABC approaches
ARFIMA 长记忆过程的新贝叶斯算法:MCMC 与 ABC 的比较
This paper presents a comparative study of two Bayesian approaches - Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC) - for estimating the parameters of autoregressive fractionally-integrated moving average (ARFIMA) models, which are widely used to capture long-memory in time series data. We propose a novel MCMC algorithm that filters the time series into distinct long-memory and ARMA components, and benchmarked it against standard approaches.…